MTS Systems Design Eng. Job Listing at AMD in Hyderabad, TS (Job ID 55214-en-us)

Description

WHAT YOU DO AT AMD CHANGES EVERYTHING

We care deeply about transforming lives with AMD technology to enrich our industry, our communities, and the world. Our mission is to build great products that accelerate next-generation computing experiences – the building blocks for the data center, artificial intelligence, PCs, gaming and embedded. Underpinning our mission is the AMD culture. We push the limits of innovation to solve the world's most important challenges. We strive for execution excellence while being direct, humble, collaborative, and inclusive of diverse perspectives. 

AMD together we advance_

MTS SYSTEMS DESIGN ENGINEER  

THE ROLE:

We are looking for a dynamic, energetic Lead / Senior Runtime Stack development Engineer to join our growing team in AI group. As an ML Runtime Stack Developer, you will be responsible for designing, developing, and optimizing the runtime components on AMD's XDNA Neural Processing Units that power cutting edge generative models like Stable diffusion, SDXL-Turbo, Llama2, etc. Your work will directly impact the efficiency, scalability, and reliability of our ML applications. If you thrive in a fast-paced environment and love working on cutting edge machine learning inference, this role is for you.

KEY RESPONSIBILITIES:

1. Runtime Wrapper Development:
   • Design and implement C++ runtime wrappers, APIs, and frameworks for ML model execution.
   • Collaborate with kernel developers to integrate ML operators seamlessly into high level ML frameworks.
2. Model Loading and Inference:
   • Interface with ONNX / Pytorch runtime engines to deploy the model on CPUs.
   • Develop efficient model loading mechanisms to minimize startup latency.
   • Implement high-performance inference engines for Client GenAI workloads such as Llama2-7B, Stable diffusion, SDXL-Turbo etc.
3. Resource Management:
   • Manage CPU, and memory resources effectively during model execution.
   • Handle resource allocation for ML deployments across different tenants.
4. Scalability and Optimization:
   • Architect optimized CPU alternative implementation for ML operators that are not supported on NPUs.
5. Monitoring and Diagnostics:
   • Build tools to track resource utilization, bottlenecks, and anomalies.
   • Enable detailed profiling and debugging tools for analyzing ML workload latency.
6. Collaboration and Documentation:
   • Work closely with ML researchers, software engineers, and Architecture teams to understand the performance requirements.
   • Document design specs, APIs, and follow coding guidelines like creating PRs, and doing diligent code reviews.

PREFERRED EXPERIENCE:

• Strong programming skills in C++, Python.
• Experience with ML frameworks (e.g., TensorFlow, PyTorch) is required.
• Experience with ML models such as CNN, LSTM, LLMs, Diffusion is a must.
• Experience with ONNX, Pytorch runtime stacks is a must.
• Knowledge and parallel computing is a bonus.
• Familiarity with containerization (Docker, Anaconda, etc) is good to have.
• Excellent problem-solving abilities and a passion for performance optimization.

ACADEMIC CREDENTIALS:

• BS with 7 years or MS with 5 years of exp
• PhD in Electrical Engineering or Computer Engineering with 3 years of experience

#LI-MK1

Benefits offered are described:  AMD benefits at a glance.

AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law.   We encourage applications from all qualified candidates and will accommodate applicants' needs under the respective laws throughout all stages of the recruitment and selection process.